Research Papers:
Mutation based treatment recommendations from next generation sequencing data: a comparison of web tools
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Jaymin M. Patel1, Joshua Knopf1, Eric Reiner3, Veerle Bossuyt2, Lianne Epstein1, Michael DiGiovanna1, Gina Chung1, Andrea Silber1, Tara Sanft1, Erin Hofstatter1, Sarah Mougalian1, Maysa Abu-Khalaf1, James Platt1, Weiwei Shi1, Peter Gershkovich2, Christos Hatzis1, Lajos Pusztai1
1Medical Oncology, Yale Cancer Center, Yale School of Medicine, CT 06520, New Haven, USA
2Pathology, Yale Cancer Center, Yale School of Medicine, CT 06520, New Haven, USA
3Radiology, Yale Cancer Center, Yale School of Medicine, CT 06520, New Haven, USA
Correspondence to:
Lajos Pusztai, e-mail: [email protected]
Keywords: breast cancer, biomarkers and intervention studies, mutation based treatment recommendations, tumor profiling, personalized medicine
Received: December 02, 2015 Accepted: February 23, 2016 Published: March 09, 2016
ABSTRACT
Interpretation of complex cancer genome data, generated by tumor target profiling platforms, is key for the success of personalized cancer therapy. How to draw therapeutic conclusions from tumor profiling results is not standardized and may vary among commercial and academically-affiliated recommendation tools. We performed targeted sequencing of 315 genes from 75 metastatic breast cancer biopsies using the FoundationOne assay. Results were run through 4 different web tools including the Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal, for drug and clinical trial recommendations. These recommendations were compared amongst each other and to those provided by FoundationOne. The identification of a gene as targetable varied across the different recommendation sources. Only 33% of cases had 4 or more sources recommend the same drug for at least one of the usually several altered genes found in tumor biopsies. These results indicate further development and standardization of broadly applicable software tools that assist in our therapeutic interpretation of genomic data is needed. Existing algorithms for data acquisition, integration and interpretation will likely need to incorporate artificial intelligence tools to improve both content and real-time status.